My Journey Through B.Tech at IIT Kharagpur: Electrical Engineering & Computer Science (2016-2020)

Four Years of Discovery, Growth, and Engineering Excellence

The Beginning: JEE Advanced and the Road to IIT KGP

July 2016. The JEE Advanced results were out, and I’d secured a rank that opened doors to the prestigious Indian Institutes of Technology. Among all the choices, IIT Kharagpur stood out—established in 1950, India’s first IIT, with a legacy of producing engineers who shaped the nation’s technological landscape.

Choosing my specialization wasn’t straightforward. Like most JEE toppers, I was initially eyeing pure Computer Science or core Electrical Engineering. But during the counseling process, I discovered the unique interdisciplinary opportunities at IIT KGP that allowed me to pursue both electrical engineering fundamentals and computer science applications through a combination of core EE courses and strategic electives in computing.

The deciding moment came during a conversation with a senior who worked at Intel. “The future belongs to engineers who understand both hardware and software,” he said. “Electrical engineering gives you the foundation of how systems work at the physical level—circuits, signals, power, control systems. Computer science teaches you how to build intelligence on top of that foundation. The combination is incredibly powerful.”

I realized I could structure my studies to gain deep expertise in electrical engineering while building substantial computer science knowledge through electives and projects. This interdisciplinary approach would position me uniquely for the converging worlds of hardware and software.

First Year: The Foundation (2016-17)

Walking into the sprawling IIT Kharagpur campus in August 2016 was overwhelming. The 2100-acre campus, with its mix of modern and heritage buildings, felt like a small city. I was allotted to Azad Hall (Azad Hall), one of the 16 halls of residence that form the backbone of IIT KGP’s vibrant campus life.

Semester 1: Building the Base

The first semester curriculum was common across all engineering branches—a foundation that would prepare us for specialized studies ahead.

Mathematics-I with Professor S.K. Neogy was my first taste of rigorous mathematical analysis. We covered multivariable calculus, infinite series, and vector analysis—mathematical tools that would prove essential for understanding electromagnetic fields, signal processing, and machine learning algorithms later in my studies. The jump from Class 12 mathematics to IIT-level rigor was steep, but it established the analytical thinking that would serve me throughout my engineering education.

Physics-I introduced quantum mechanics and wave optics at a level that made Class 12 physics seem elementary. Understanding quantum mechanics would later help me grasp semiconductor device physics, while wave optics provided insights into fiber optic communications and laser technology. The laboratory sessions were particularly enlightening—measuring the wavelength of light using Lloyd’s mirror and studying interference patterns gave me hands-on experience with fundamental physics principles.

Engineering Drawing and Computer Graphics was unexpectedly valuable. Hand-drawing technical diagrams with precision, understanding orthographic projections, and learning AutoCAD basics taught me the importance of visualization in engineering. This skill would later prove crucial when designing circuit layouts, understanding system architectures, and creating technical documentation.

Programming and Data Structures was my first formal introduction to computer science. We used C programming language, and I was fascinated by how logical thinking could be translated into code. The laboratory sessions involved implementing basic algorithms and data structures—sorting algorithms, linked lists, stacks, and queues. This foundation would become increasingly important as I pursued the software side of my interdisciplinary goals.

Semester 2: Expanding Horizons

The second semester continued building foundational knowledge while introducing more specialized topics.

Mathematics-II covered differential equations, Laplace transforms, and Fourier series—mathematical tools that would become essential for signal processing, control systems, and communication engineering. Professor R.K. Jana’s approach to teaching Fourier analysis was particularly memorable. He connected abstract mathematical concepts to real-world applications, showing how Fourier transforms are used in everything from image processing to wireless communication.

Electrical Technology was my first taste of core electrical engineering. We studied basic circuits, AC/DC analysis, Kirchhoff’s laws, and power systems fundamentals. The laboratory sessions involved building simple circuits on breadboards, measuring voltages and currents with multimeters, and understanding the behavior of resistors, capacitors, and inductors. This hands-on experience was crucial for developing intuition about electrical systems that would support both my EE studies and my understanding of computer hardware.

Chemistry covered atomic structure, chemical bonding, and thermodynamics. While not directly related to electrical engineering, it provided essential knowledge about materials science that would prove valuable when studying semiconductor devices and integrated circuit fabrication.

Introduction to Manufacturing Processes exposed us to machining, casting, welding, and other manufacturing techniques. This knowledge proved valuable when understanding how electronic components are manufactured and when designing systems that needed to interface with mechanical components.

Second Year: Diving Deeper (2017-18)

Semester 3: The Specialization Begins

Third semester marked the beginning of my focused pursuit of electrical engineering with computer science integration.

Transform Calculus was mathematically intensive, covering Laplace transforms, Z-transforms, and Fourier transforms in detail. These mathematical tools are fundamental to signal processing, control systems, and digital communication—core areas where electrical engineering and computer science intersect. Professor A. Banerjee’s approach was to solve numerous practical problems, helping us understand how these transforms are used in digital signal processing algorithms and communication systems.

Basic Electronics was my introduction to semiconductor devices and electronic circuits. We studied diodes, transistors, amplifiers, and digital logic circuits. The laboratory sessions involved building amplifier circuits, understanding frequency response, and working with oscilloscopes and function generators. This course was crucial for understanding how computer hardware works at the transistor level.

Signals and Networks introduced concepts of signals, systems, and network analysis. We learned about time and frequency domain analysis, convolution, and filtering—fundamental concepts for understanding both analog signal processing and digital signal processing algorithms. This course bridged the gap between mathematical theory and practical engineering applications.

Programming and Data Structures Tutorial and Laboratory expanded on first-year programming with more advanced data structures and algorithms. We implemented binary trees, graphs, hash tables, and studied algorithm complexity analysis. This deeper computer science foundation would prove essential for my later work in machine learning and systems programming.

Semester 4: Electronics and Digital Systems

Fourth semester deepened my understanding of electronics while introducing digital systems concepts.

Analog Circuits covered the design and analysis of electronic circuits using operational amplifiers, transistors, and other active devices. We designed amplifiers, filters, oscillators, and voltage regulators. The laboratory sessions involved building and testing these circuits, learning to use spectrum analyzers and network analyzers. This course was crucial for understanding how analog signal processing works and how it interfaces with digital systems.

Basic Electronics Laboratory provided extensive hands-on experience with electronic circuits and measurement instruments. These laboratory sessions taught me to debug circuits, use sophisticated test equipment, and understand the practical limitations of theoretical circuit analysis.

Measurements and Electronic Instruments covered the principles of electronic measurement systems. We studied different types of sensors, signal conditioning circuits, data acquisition systems, and measurement techniques. This course was particularly relevant to the intersection of electrical engineering and computer science, as modern measurement systems increasingly rely on digital signal processing and computer interfaces.

Numerical Solution of Ordinary and PDE introduced computational methods for solving complex mathematical problems that arise in engineering. This course bridged mathematics and computer science, teaching me how to implement numerical algorithms and use computational tools for engineering problem-solving.

Third Year: Core Electrical Engineering with Computing Focus (2018-19)

Semester 5: Power Systems and Signal Processing

Fifth semester was where electrical engineering fundamentals truly came together with computational applications.

Mass Transfer covered the principles of heat and mass transfer, essential for understanding thermal management in electronic systems and power devices. This knowledge proved valuable when studying power electronics and thermal design of computer systems.

Circuit Electronics went deeper into advanced electronic circuits, including power electronics, switching circuits, and high-frequency circuit design. Understanding switching circuits and power conversion was crucial for appreciating how computer power supplies work and how energy efficiency is achieved in electronic systems.

Control System Engineering was one of the most important courses for my interdisciplinary goals. We studied feedback control theory, stability analysis, controller design, and system optimization. Professor M. Sengupta’s approach combined theoretical rigor with practical examples from industrial automation and robotic systems. The laboratory sessions involved implementing PID controllers and analyzing system response using MATLAB—my first serious exposure to engineering software that would become essential for both electrical engineering design and algorithm development.

Electromagnetic Engineering covered Maxwell’s equations, electromagnetic wave propagation, and antenna theory. This fundamental understanding of electromagnetics was crucial for understanding wireless communication systems, RF circuit design, and electromagnetic compatibility in electronic systems.

Introduction to Wireless Communications exposed us to modern communication systems—modulation techniques, channel coding, multiple access schemes, and network protocols. This course perfectly exemplified the intersection of electrical engineering and computer science, showing how signal processing algorithms and networking protocols enable modern wireless systems.

Semester 6: Advanced Systems and Digital Signal Processing

Sixth semester built on previous knowledge while introducing more advanced concepts that bridged electrical engineering and computer science.

Audio and Video Engineering covered signal processing techniques used in multimedia systems. We studied compression algorithms, digital filters, and real-time processing constraints. This course demonstrated how electrical engineering principles (signal processing) combine with computer science algorithms (compression, real-time systems) to create practical multimedia systems.

Embedded Systems was crucial for understanding modern electronic systems. We learned about microcontrollers, embedded programming, real-time operating systems, and hardware-software integration. The laboratory sessions involved programming microcontrollers for various applications—data acquisition, motor control, and communication interfaces. This course was pivotal in showing me how software and hardware work together in modern systems.

Embedded Systems Laboratory provided extensive hands-on experience with embedded system design. Projects included building temperature controllers, data loggers, and simple automation systems. These projects required understanding both the electrical engineering aspects (sensor interfaces, power management) and computer science aspects (real-time programming, algorithm implementation).

Instrumentation Devices and Laboratory covered various sensors and measurement systems, combining my electrical engineering foundation with practical system design. We worked with temperature sensors, pressure sensors, strain gauges, and learned how to design complete measurement systems including signal conditioning, data acquisition, and software interfaces.

Final Year: Specialization and Advanced Topics (2019-20)

Semester 7: Advanced Signal Processing and Intelligent Systems

Seventh semester allowed me to focus on advanced topics that combined electrical engineering with computer science and artificial intelligence.

Soft Computing and Applications introduced artificial intelligence and machine learning techniques used in electrical engineering applications. We covered neural networks, fuzzy logic, genetic algorithms, and their applications in control systems, power system optimization, and signal processing. This was my first formal exposure to AI concepts that would later prove crucial for my graduate studies.

Digital Signal Processing was one of the most mathematically intensive and rewarding courses. We covered discrete-time signals, digital filters, FFT algorithms, spectral analysis, and adaptive filtering. Professor K. Das emphasized both theoretical understanding and practical implementation using MATLAB and dedicated DSP processors. This course was the perfect synthesis of electrical engineering (signal processing theory) and computer science (algorithm implementation and optimization).

Natural Language Processing was an elective that broadened my perspective beyond traditional electrical engineering. This course showed me how signal processing techniques could be applied to speech and language, opening my eyes to the broader applications of electrical engineering principles in computer science domains.

Project-I was my first independent research project. I worked on “Design of an Intelligent Power Management System” under Professor S. Mukhopadhyay’s guidance. The project involved developing a smart grid monitoring system that used wireless sensor networks, real-time data processing, and machine learning algorithms for load forecasting. It required integrating electrical engineering knowledge (power systems, sensors) with computer science skills (networking, data processing, machine learning algorithms).

Industrial Training during the summer break at Bharat Electronics Limited (BEL) exposed me to how advanced electronic systems are developed in industry. I worked on radar signal processing systems, learning about real-time signal processing implementations, hardware-software co-design, and the challenges of deploying complex algorithms on embedded platforms.

Semester 8: Capstone and Future Preparation

The final semester was focused on completing major projects and preparing for graduate studies.

Business Dynamics and Control provided insights into how engineering decisions affect business outcomes—valuable for engineers who aspire to leadership roles in technology companies.

Principles of Programming Languages deepened my computer science knowledge, covering different programming paradigms, compiler design, and language implementation. This course provided theoretical foundations that would prove valuable for graduate studies in computer science.

Machine Learning was an elective that provided formal introduction to ML algorithms and their applications in electrical engineering. We covered supervised and unsupervised learning, neural networks, statistical learning theory, and applications to signal processing and control systems. Professor A. Ray’s approach emphasized both mathematical foundations and practical implementation, showing how machine learning could be applied to traditional electrical engineering problems like fault detection, system identification, and optimization.

High Performance Parallel Programming introduced parallel computing concepts and GPU programming using CUDA. This course was directly relevant to the computational demands of modern signal processing and machine learning applications. We learned to implement parallel algorithms for matrix operations, signal processing, and optimization—skills that would prove essential for research in computational systems.

Project-II was my capstone project: “Development of an Adaptive Communication System Using Machine Learning.” Building on my previous project, I developed a wireless communication system that could adaptively modify its parameters based on channel conditions using reinforcement learning algorithms. The project required extensive programming, RF circuit design, software-defined radio implementation, and performance optimization.

Comprehensive Viva Voce was the final evaluation covering the entire four-year curriculum, testing both the breadth of electrical engineering knowledge and the depth of my specialized focus areas in signal processing and computational systems.

Beyond the Classroom: The Complete IIT Experience

Research and Innovation

Beyond coursework, I engaged with faculty on research projects that bridged electrical engineering and computer science. Working with Professor S. Mukhopadhyay on intelligent power systems introduced me to academic research methodology. I learned to formulate research questions, conduct literature reviews, design experiments, and present results.

This research experience culminated in two conference papers: “Machine Learning Approaches for Smart Grid Optimization” presented at the IEEE Power and Energy Society General Meeting, and “Adaptive Signal Processing for Wireless Sensor Networks” at the National Conference on Communications (NCC).

Technical Competitions and Projects

Kshitij, IIT KGP’s annual technology festival, provided opportunities to participate in technical competitions that combined electrical engineering and computer science knowledge. I participated in robotics competitions (requiring both hardware design and intelligent control algorithms), electronic design contests, and programming competitions.

Inter-IIT Technical Meet competitions allowed me to work on challenging projects with teammates from different engineering disciplines. Our team developed an autonomous drone for agricultural monitoring, combining electrical engineering (sensor systems, power management), computer science (computer vision, path planning), and mechanical engineering (flight dynamics).

Industry Interactions and Internships

Summer internships at ISRO (Satellite Centre) and Texas Instruments provided exposure to how electrical engineering and computer science intersect in high-technology industries. At ISRO, I worked on satellite communication systems, learning about space-qualified electronics and real-time signal processing. At TI, I contributed to digital signal processor development, understanding how DSP algorithms are implemented efficiently in hardware.

Guest lectures by industry professionals from companies like Intel, Qualcomm, and Microsoft provided insights into career opportunities at the intersection of hardware and software development.

Key Learning Experiences and Transformative Moments

The Signal Processing Revelation

The breakthrough moment in my understanding came during the Digital Signal Processing course in seventh semester. Working on a project to implement adaptive noise cancellation for communication systems, I suddenly understood how mathematics, electrical engineering, and computer science converge to solve real-world problems.

Implementing the adaptive filter algorithm, optimizing it for real-time execution, and seeing it successfully remove interference from communication signals was magical. It demonstrated the power of combining theoretical knowledge from multiple disciplines to create practical solutions.

The Machine Learning Integration

Taking Machine Learning as an elective opened my eyes to how AI could enhance traditional electrical engineering systems. A course project on using neural networks for power system fault detection showed me how modern computational techniques could solve classical electrical engineering problems more effectively than traditional methods.

This experience convinced me that the future of electrical engineering would be increasingly intertwined with artificial intelligence and machine learning.

The Embedded Systems Evolution

Working on embedded systems projects showed me how modern electrical engineering systems are fundamentally software-hardware co-designs. My final year project on adaptive communication systems required deep understanding of both RF circuit design and machine learning algorithms implemented on embedded platforms.

This integration of hardware expertise and software intelligence represented the direction that electrical engineering was heading—toward intelligent, adaptive systems that could learn and optimize their performance.

The Broader Perspective: Engineering as Problem-Solving

Four years at IIT Kharagpur taught me that engineering is fundamentally about problem-solving using mathematical, scientific, and computational tools. My interdisciplinary approach combining electrical engineering with computer science provided a unique perspective on how modern systems work.

Systems Thinking

Perhaps the most valuable skill developed was systems thinking—understanding how different components (hardware, software, algorithms, interfaces) interact to achieve overall system objectives. This perspective proved crucial for designing complex modern systems that span multiple engineering disciplines.

Computational Engineering

The integration of computer science with electrical engineering taught me that modern engineering is increasingly computational. Whether designing power systems, communication networks, or control systems, computational tools and algorithms are essential for both design and operation.

Interdisciplinary Problem-Solving

Working at the intersection of electrical engineering and computer science developed skills in translating problems between different domains. Understanding how to formulate electrical engineering problems in computational terms, and how to implement computational solutions using electrical engineering principles, became a key differentiator.

Preparing for the Future: Graduate Studies and Career

By the final year, I was convinced that the most exciting opportunities lay at the intersection of electrical engineering, computer science, and emerging fields like machine learning and data science. This realization shaped my decision to pursue graduate studies that would build on both foundations.

Graduate School Preparation

Courses like Machine Learning, High Performance Parallel Programming, and Digital Signal Processing provided essential background for graduate studies in computer science. The research experience and technical writing skills developed through projects prepared me for the research-intensive nature of graduate school.

My strong foundation in both electrical engineering fundamentals and computer science principles made me an attractive candidate for interdisciplinary graduate programs.

Industry Readiness

The comprehensive curriculum also prepared me for roles in technology companies working on hardware-software systems. Understanding both the electrical engineering aspects (circuits, signals, power, electromagnetics) and computer science aspects (algorithms, programming, systems) made me valuable for roles in companies developing complex technological products.

Reflections: The IIT Kharagpur Impact

Looking back, four years at IIT Kharagpur transformed me from a curious high school student into a confident engineer capable of working across traditional disciplinary boundaries.

Technical Breadth and Depth

The rigorous curriculum in electrical engineering, combined with strategic computer science electives, developed both breadth and depth. I could understand systems from the physics level (quantum mechanics, electromagnetics) up to the algorithm level (machine learning, signal processing, networking).

Research and Innovation Mindset

The emphasis on projects, research experience, and independent learning created a mindset that approaches technical challenges with curiosity and systematic investigation. This approach has proven valuable in both academic research and industry innovation.

Network and Community

The relationships formed with classmates, faculty, and alumni created a network spanning both electrical engineering and computer science industries. The IIT KGP community provided access to diverse career paths and continued learning opportunities.

Adaptability and Continuous Learning

Perhaps most importantly, the interdisciplinary education taught me to be adaptable and comfortable with continuous learning. The technology landscape evolves rapidly, and the ability to learn new concepts and integrate knowledge from different fields has been essential for staying relevant.

Final Thoughts: The Foundation for Innovation

My B.Tech experience at IIT Kharagpur, combining electrical engineering with computer science, provided the foundation for everything that followed—graduate studies in computer science and quantitative finance, research in machine learning systems, and career in technology.

The interdisciplinary approach, emphasizing both hardware understanding and software capabilities, created a unique educational experience that prepared me for the rapidly evolving technology landscape where the boundaries between electrical engineering and computer science continue to blur.

Most importantly, IIT Kharagpur taught me that engineering is not just about technical skills—it’s about using those skills to solve problems that matter, to build systems that improve lives, and to push the boundaries of what’s possible through the intelligent integration of multiple engineering disciplines.

The four years from 2016-2020 were challenging, rewarding, and transformative. They established the foundation for lifelong learning and professional growth at the exciting intersection of electrical engineering and computer science.

This story represents my journey through one of India’s most prestigious engineering institutions, where I learned to bridge traditional electrical engineering with modern computer science, creating a unique perspective that has guided my career in technology and research.